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opencv feature matching

using ORB and Matcher

#orb   #matching   #opencv  

Table of Content

Hamming Distance#

Hamming Distance measures the similarity between two strings of the same length. Hamming distance is the number of bit positions in which the two bits are different.

BFMatcher#

Brute-Force matching take each feature from image query and compares it to all the other/train image

Matcher result#

match method

DMatch

  • distance
  • imgIdx: train image index
  • queryIdx: query descriptor index
  • trainIdx: train descriptor index

Basic Demo#

import numpy as np
import cv2


# Read the query image as query_img
# and train image This query image
# is what you need to find in train image
# Save it in the same directory
# with the name image.jpg 
query_img = cv2.imread('query.jpg')
train_img = cv2.imread('train.jpg')

# Convert it to grayscale
query_img_bw = cv2.cvtColor(query_img,cv2.COLOR_BGR2GRAY)
train_img_bw = cv2.cvtColor(train_img, cv2.COLOR_BGR2GRAY)

# Initialize the ORB detector algorithm
orb = cv2.ORB_create()

# Now detect the keypoints and compute
# the descriptors for the query image
# and train image
queryKeypoints, queryDescriptors = orb.detectAndCompute(query_img_bw, mask=None)
trainKeypoints, trainDescriptors = orb.detectAndCompute(train_img_bw, mask=None)

# Initialize the Matcher for matching
# the keypoints and then match the
# keypoints
matcher = cv2.BFMatcher()
matches = matcher.match(queryDescriptors,trainDescriptors)

# draw the matches to the final image
# containing both the images the drawMatches()
# function takes both images and keypoints
# and outputs the matched query image with
# its train image
final_img = cv2.drawMatches(query_img, queryKeypoints,
train_img, trainKeypoints, matches[:20],None)

final_img = cv2.resize(final_img, (1000,650))

# Show the final image
cv2.imshow("Matches", final_img)
cv2.waitKey(3000)
#

Reference#